A computation scheme based on field attenuation rate for improving regional-residual separation of potential field data set

Abstract To further improve the accuracy of regional-residual separation of potential field data set, this paper presents a novel computation scheme based on different attenuation rate of the fields induced from deep and shallow sources respectively. For the new scheme, the observations are first upward continued to a plane above it to get an updated field. Then, the difference between the original field and the updated field is calculated. Next, a controlling parameter is set to select those data points whose amplitudes have been much reduced. The adverse effects from the residual anomalies on the fitting of the regional trend can be reduced by removing the identified local points from the original field. Finally, a low-order polynomial is utilised for approximating the regional trend, and the corresponding residual field can be obtained by simple subtraction. Compared with gradient-based methods, the proposed new scheme has better noise adaptability for distinguishing different anomalies. The accuracy of the presented scheme was tested on synthetic data with and without noise. All tests showed that the new scheme reduces subjectivity and inaccuracy of the conventional methods significantly. In addition, the scheme was applied to Bouguer gravity anomaly of the Dida orebodies in Jilin Province, northeast China. This application also verified the superiority of the proposed scheme.

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Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽

10.1190/geo2013-0087.1 ◽

2014 ◽

Vol 79 (1) ◽

pp. IM1-IM9 ◽

Cited By ~ 20

Author(s):

Nathan Leon Foks ◽

Richard Krahenbuhl ◽

Yaoguo Li

Keyword(s):

Potential Field ◽

Adaptive Sampling ◽

Field Data ◽

Computational Cost ◽

Large Field ◽

Magnetic Data ◽

Data Sampling ◽

Data Types ◽

Data Set ◽

Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

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Geophysical tutorial: Euler deconvolution of potential-field data

The Leading Edge ◽

10.1190/tle33040448.1 ◽

2014 ◽

Vol 33 (4) ◽

pp. 448-450 ◽

Cited By ~ 6

Author(s):

Leonardo Uieda ◽

Vanderlei C. Oliveira ◽

Valéria C. F. Barbosa

Keyword(s):

Open Source ◽

Potential Field ◽

Field Data ◽

Synthetic Data ◽

Euler Deconvolution ◽

Potential Field Data ◽

Python Package

In this tutorial, we will talk about a widely used method of interpretation for potential-field data called Euler de-convolution. Our goal is to demonstrate its usefulness and, most important, to call attention to some pitfalls encountered in interpretation of the results. The code and synthetic data required to reproduce our results and figures can be found in the accompanying IPython notebooks ( ipython.org/notebook ) at dx.doi.org/10.6084/m9.figshare.923450 or github.com/pinga-lab/paper-tle-euler-tutorial . The note-books also expand the analysis presented here. We encourage you to download the data and try them on your software of choice. For this tutorial, we will use the implementation in the open-source Python package Fatiando a Terra ( fatiando.org ).

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Reduction of potential field data to a horizontal plane

Geophysics ◽

10.1190/1.1442866 ◽

1990 ◽

Vol 55 (5) ◽

pp. 549-555 ◽

Cited By ~ 25

Author(s):

Mark Pilkington ◽

W. E. S. Urquhart

Keyword(s):

Potential Field ◽

Horizontal Plane ◽

Field Data ◽

Synthetic Data ◽

Mirror Image ◽

Source Distribution ◽

Aeromagnetic Survey ◽

Potential Field Data ◽

Equivalent Sources ◽

Topographic Gradients

Most existing techniques for potential field data enhancement and interpretation require data on a horizontal plane. Hence, when observations are made on an irregular surface, reduction to a horizontal plane is necessary. To effect this reduction, an equivalent source distribution that models the observed field is computed on a mirror image of the observation surface. This irregular mirror image surface is then replaced by a horizontal plane and the effect of the equivalent sources is computed on the required horizontal level. This calculated field approximates the field reduced to a horizontal plane. The good quality of this approximation is demonstrated by two‐dimensional synthetic data examples in which the maximum errors occur in areas of steep topographic gradients and increased magnetic field intensity. The approach is also applied to a portion of a helicopter‐borne aeromagnetic survey from the Gaspé region in Quebec, Canada, where the results are a horizontal shifting of anomaly maxima of up to 150 m and changes in anomaly amplitudes of up to 100 nT.

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3-D inversion of gravity and magnetic data with depth resolution

Geophysics ◽

10.1190/1.1444550 ◽

1999 ◽

Vol 64 (2) ◽

pp. 452-460 ◽

Cited By ~ 62

Author(s):

Maurizio Fedi ◽

Antonio Rapolla

Keyword(s):

Potential Field ◽

Field Data ◽

Vertical Direction ◽

Depth Resolution ◽

Magnetic Data ◽

Data Set ◽

Potential Field Data ◽

Magnetic Methods ◽

Level Data ◽

Gravity And Magnetic

Magnetization and density models with depth resolution are obtained by solving an inverse problem based on a 3-D set of potential field data. Such a data set is built from information on vertical and horizontal variations of the magnetic or gravity field. The a priori information consists of delimiting a source region and subdividing it in a set of blocks. In this case, the information related to a set of field data along the vertical direction is not generally redundant and is decisive in giving a depth resolution to the gravity and magnetic methods. Because of this depth resolution, which derives solely from the potential field data, an unconstrained and joint inversion of a multiobservation‐level data set is shown to provide surprising results for error‐free synthetic data. On the contrary, a single‐observation level data inversion produces an incorrect and too shallow model. Hence, a good depth resolution is likely to occur for the gravity and magnetic methods when based on the information along the vertical direction. This is also evidenced by an analysis of the kernel function versus the field altitude level and by a singular value analysis of the inversion operators for both the single and multilevel cases. Errors connected to numerical upward continuation do not affect the quality of the solution, provided that the data set extent is larger than that of the anomaly field. Application of the method to a 3-D magnetic data set relative to Vesuvius indicates that the method may significantly improve interpretation of potential fields.

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OpenMP Implementation of a Novel Potential-Field-Data Source-Growth-Based Inversion Approach for 3D Salt Imaging in Deepwater Gulf of Mexico

Applied Sciences ◽

10.3390/app10144798 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4798

Author(s):

Naín Vera ◽

Carlos Couder-Castañeda ◽

Jorge Hernández ◽

Alfredo Trujillo-Alcántara ◽

Mauricio Orozco-del-Castillo ◽

...

Keyword(s):

Gulf Of Mexico ◽

Potential Field ◽

Field Data ◽

Three Dimensional ◽

Synthetic Data ◽

Complex Salt ◽

Research Field ◽

Inversion Method ◽

Potential Field Data ◽

Simultaneous Inversion

Potential-field-data imaging of complex geological features in deepwater salt-tectonic regions in the Gulf of Mexico remains an open active research field. There is still a lack of resolution in seismic imaging methods below and in the surroundings of allochthonous salt bodies. In this work, we present a novel three-dimensional potential-field-data simultaneous inversion method for imaging of salt features. This new approach incorporates a growth algorithm for source estimation, which progressively recovers geological structures by exploring a constrained parameter space; restrictions are posed from a priori geological knowledge of the study area. The algorithm is tested with synthetic data corresponding to a real complex salt-tectonic geological setting commonly found in exploration areas of deepwater Gulf of Mexico. Due to the huge amount of data involved in three-dimensional inversion of potential field data, the use of parallel computing techniques becomes mandatory. In this sense, to alleviate computational burden, an easy to implement parallelization strategy for the inversion scheme through OpenMP directives is presented. The methodology was applied to invert and integrate gravity, magnetic and full tensor gradient data of the study area.

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Structural, petrophysical, and geological constraints in potential field inversion using the Tomofast-x v1.0 open-source code

Geoscientific Model Development ◽

10.5194/gmd-14-6681-2021 ◽

2021 ◽

Vol 14 (11) ◽

pp. 6681-6709

Author(s):

Jérémie Giraud ◽

Vitaliy Ogarko ◽

Roland Martin ◽

Mark Jessell ◽

Mark Lindsay

Keyword(s):

Open Source ◽

Potential Field ◽

Joint Inversion ◽

Synthetic Data ◽

Source Inversion ◽

Bound Constraints ◽

Data Set ◽

Potential Field Data ◽

Geological Information ◽

Geological Constraints

Abstract. The quantitative integration of geophysical measurements with data and information from other disciplines is becoming increasingly important in answering the challenges of undercover imaging and of the modelling of complex areas. We propose a review of the different techniques for the utilisation of structural, petrophysical, and geological information in single physics and joint inversion as implemented in the Tomofast-x open-source inversion platform. We detail the range of constraints that can be applied to the inversion of potential field data. The inversion examples we show illustrate a selection of scenarios using a realistic synthetic data set inspired by real-world geological measurements and petrophysical data from the Hamersley region (Western Australia). Using Tomofast-x's flexibility, we investigate inversions combining the utilisation of petrophysical, structural, and/or geological constraints while illustrating the utilisation of the L-curve principle to determine regularisation weights. Our results suggest that the utilisation of geological information to derive disjoint interval bound constraints is the most effective method to recover the true model. It is followed by model smoothness and smallness conditioned by geological uncertainty and cross-gradient minimisation.

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An improved and stable downward continuation of potential field data: The truncated Taylor series iterative downward continuation method

Geophysics ◽

10.1190/geo2012-0463.1 ◽

2013 ◽

Vol 78 (5) ◽

pp. J75-J86 ◽

Cited By ~ 18

Author(s):

HengLei Zhang ◽

Dhananjay Ravat ◽

XiangYun Hu

Keyword(s):

Taylor Series ◽

Potential Field ◽

Field Data ◽

Memory Performance ◽

Continuation Method ◽

Taylor Series Expansion ◽

Downward Continuation ◽

Continuation Methods ◽

Potential Field Data ◽

The Difference

We present a stable downward continuation strategy based on combining the ideas of the Taylor series expansion and the iterative downward continuation methods in a single method with better downward continuation and/or computer time/memory performance for potential field data containing noise. In the new truncated Taylor series iterative downward continuation (TTSIDC) method, a correction is made on the continuing plane by downward continuing the difference between the observed and the calculated field. The process is iteratively repeated until the difference meets the convergence conditions. It is tested on synthetic and field data and compared to other downward continuation methods. The proposed method yields sharper images and estimates more accurate amplitudes than most of the existing methods, especially for downward continuation over larger distances. The TTSIDC method also gives comparable results to the method of downward continuation using the least-squares inversion (DCLSI); however, the DCLSI method’s requirements of computer memory and time are substantially greater than our TTSIDC method, rendering the DCLSI method impractical for data sets of routine size on desktop computers commonly available today.

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Backus-Gilbert inversion of potential field data in the frequency domain and its application to real and synthetic data

Geofísica Internacional ◽

10.22201/igeof.00167169p.1994.33.4.587 ◽

1994 ◽

Vol 33 (4) ◽

pp. 531-539

Author(s):

Uwe Koppelt ◽

Javier Rojas

Keyword(s):

Frequency Domain ◽

Potential Field ◽

Field Data ◽

Synthetic Data ◽

Potential Field Data

Se presenta un algoritmo para la inversión de datos del campo potencial en el dominio de las frecuencias utilizando la transformación de Backus-Gilbert. Se describe uno de los problemas fundamentales en todo proceso de interpretación geofísica como es la solución del problema directo y del problema inverso. La comparación de los resultados en el domino del espacio y de las frecuencias muestra las ventajas del algoritmo aquí presentado. Se demuestra la efectividad del algoritmo solucionado tareas de geofísica ambiental como la detección de depósitos antiguos de desechos industriales. Esta técnica interpretativa es aplicable también a la interpretación de investigaciones geofísicas en sitios arqueológicos.

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